Abstract
The analysis of vegetation dynamics and agricultural production is essential in semi-arid regions, in particular as a consequence of the frequent occurrence of periods of drought. In this paper, a multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION (between September 1998 and August 2013) and TERRA-MODIS satellite data (between September 2000 and August 2013), was used to analyze the vegetation dynamics over the central region of Tunisia in North Africa, which is characterized by a semi-arid climate. Products derived from these two satellite sensors are generally found to be coherent. Our analysis of land use and NDVI anomalies, based on the Vegetation Anomaly Index (VAI), reveals a strong level of agreement between estimations made with the two satellites, but also some discrepancies related to the spatial resolution of these two products. The vegetation’s behavior is also analyzed during years affected by drought through the use of the Windowed Fourier Transform (WFT). Discussions of the dynamics of annual agricultural areas show that there is a combined effect between climate and farmers’ behavior, leading to an increase in the prevalence of bare soils during dry years.
Highlights
In semi-arid regions, drought is a frequent phenomenon leading to serious problems in agriculture and food safety, and increasing attention has been drawn to its high attendant economic and social costs [1]
The aim of this paper is to propose an analysis of variations in vegetation cover, in particular for the case of annual agricultural land use in a semi-arid region of North Africa, in the context of drought events
We find a more significant trend for the SPOT-VGT time series, with a slope equal to
Summary
In semi-arid regions, drought is a frequent phenomenon leading to serious problems in agriculture and food safety, and increasing attention has been drawn to its high attendant economic and social costs [1]. Drought Severity Index (PDSI; [5]) or the Standardized Precipitation Index (SPI; [6,7]). The PDSI is based on long-term historical precipitation and mean temperature data. The SPI is based on precipitation data only. In the case of regions with sparsely distributed weather stations, statistical techniques such as the Inverse Distance Weighted method or a stochastic model of Ordinary Kriging can be considered. These methods are not completely sufficient to ensure that drought quantification is achieved with high accuracy, in areas where limited ground measurements are made. The problem is more complicated in areas of the globe in which weather stations are Remote Sens. 2016, 8, 992; doi:10.3390/rs8120992 www.mdpi.com/journal/remotesensing
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